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    • Python [conda env:base] *
      • bar chart.ipynb (58de030f)
        • bar chart.ipynb
      • machine Learning.ipynb (f453bbef)
        • machine Learning.ipynb
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            bar chart.ipynb

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            • bar chart.ipynb
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            Python [conda env:base] *
            Kernel status: Idle Executed 2 cellsElapsed time: 1 second
              [1]:
              import matplotlib.pyplot as plt
              import numpy as np

              # Sample data: average temperatures over the past 10 years for five cities
              cities = ['City A', 'City B', 'City C', 'City D', 'City E']
              avg_temperatures = [22.5, 25.3, 19.8, 23.1, 21.7]

              # Create a bar chart
              fig, ax = plt.subplots()
              bars = ax.bar(cities, avg_temperatures, color=['blue', 'green', 'red', 'purple', 'orange'])

              # Add labels and title
              ax.set_xlabel('Cities')
              ax.set_ylabel('Average Temperature (°C)')
              ax.set_title('Average Temperatures of Five Cities Over the Past 10 Years')

              # Display the values on top of the bars
              for bar in bars:
              yval = bar.get_height()
              ax.text(bar.get_x() + bar.get_width()/2, yval + 0.2, round(yval, 1), ha='center', va='bottom')

              # Show the plot
              plt.show()
              [ ]:
              #A bar chart comparing average temperatures of five cities over the past 10 years.

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